Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: Systems, devices, and methods discussed herein are directed to intelligently adjusting the set of worker nodes within a computing cluster. By way of example, a computing device (or service) may monitor performance metrics of a set of worker nodes of a computing cluster. When a performance metric is detected that is below a performance threshold, the computing device may perform a first adjustment (e.g., an increase or decrease) to the number of nodes in the cluster. Training data may be obtained based at least in part on the first adjustment and utilized with supervised learning techniques to train a machine-learning model to predict future performance changes in the cluster. Subsequent performance metrics and/or cluster metadata may be provided to the machine-learning model to obtain output indicating a predicted performance change. An additional adjustment to the number of worker nodes may be performed based at least in part on the output.

Abstract: Backups and restore operations of database are customized by performing partial backups and restores. The database maintains information describing database objects that are backed up by subsequent backup operations. The data set that is backed up by backup operations can be modified by executing alter backup commands. An alter backup command identifies one or more database objects and specifies whether the database object should be excluded or included from backup operations. The alter backup command can specify database objects that do not exist in the database. These objects are included or excluded in accordance with the alter backup command whenever they are created in the database. The alter backup command can associate a criteria with a database object in order for the database object to be included or excluded in accordance with the alter backup command.

Abstract: A logical backup of a database is performed that allows physical properties of the database to be preserved upon restore. The physical properties of the database include properties related to storage of the data on the hard disk. Preserving the physical properties of a database allows performance debugging of the source database by performing performance tests on the target database rather than a production system. Incremental backup of metadata of a database is performed to minimize execution of statements that provide the final configuration of the database. Incremental backup is performed by restoring a database using a full backup and applying incremental backups to reach a particular state. The user can inspect the restored database after applying each incremental backup to determine whether the database has reached a desired state.

Abstract: Backups and restore operations of database are customized by performing partial backups and restores. The database maintains information describing database objects that are backed up by subsequent backup operations. The data set that is backed up by backup operations can be modified by executing alter backup commands. An alter backup command identifies one or more database objects and specifies whether the database object should be excluded or included from backup operations. The alter backup command can specify database objects that do not exist in the database. These objects are included or excluded in accordance with the alter backup command whenever they are created in the database. The alter backup command can associate a criteria with a database object in order for the database object to be included or excluded in accordance with the alter backup command.